AI will play an increasingly important role in the top three business objectives often cited by CEOs — greater customer intimacy, increasing competitive advantage and improving efficiency.

Janelle Hill, Gartner

From the 1980s Lisp machines and Deep Blue in 1997 to the debut of IBM’s Watson in 2010s, AI in various forms has been around for a long time. But commercial uses of AI are in specialised industry-specific applications such as actuarial forecasts and medical diagnosis — making CIOs understandably cautious about promoting AI’s potential business value.

Savvy CIOs are experimenting jointly with business peers to discover top uses and ROI for AI to evaluate its potential to disrupt markets and remake existing business models.

Here are four key insights to know before you start a successful AI journey:

Digital business is accelerating interest in AI

This is been done at a pace that has left many CIOs hurrying to build an AI strategy and investment plan appropriate for their enterprise. The rapid innovation in AI technologies has been staggering, predominantly coming from small vendors.

By following this approach, you can potentially flip the traditional engagement model between IT and the business, influencing business strategy at the outset, rather than simply developing implementation projects that follow up on the executive team’s decisions.

Market for solutions using AI in infancy

Although many core AI technologies are proven, the market for solutions using those technologies overall is in its infancy. This means you should expect rapid product and solution change.

Some industries have utilised AI to great success. In healthcare, for example, thanks to “computer-assisted diagnosis,” a computer was able to spot 52 percent of breast cancers based on mammography scans up to one year before the women were officially diagnosed.

By committing to promoting experimentation across the organisation, you can encourage your employees to interact with low cost AI products — Alexa, Cortana, a drone, a wearable, and so on. You can then actively monitor the market for emerging solutions that build on lessons learned from the experiments.

These are leading areas of rapid technology advancement, where CIOs need to build knowledge, expertise and skills. Capabilities like voice recognition, natural-language processing, image processing and others, benefit from advances in big data processing and advanced analytical methods such as machine learning and deep learning.

While most organisations may not pursue these leading-edge uses of AI, it will play an increasingly important role in the top three business objectives often cited by CEOs — greater customer intimacy, increasing competitive advantage and improving efficiency.

As a result, monitor emerging AI solutions to build out a business case and to identify the limitations in current generation technologies, so you can understand the complexity of skills needed to fill talent gaps.

Market conditions for commercial success are well aligned

Recent breakthroughs in machine learning, big data, computer vision and speech recognition are increasing the commercial potential of AI. This makes AI safe enough to investigate, experiment with and strategise about potential application domains. But AI requires new skills and a new way of thinking about problems.

You must ensure that IT owns the strategy and governance of AI solutions. Although pilot AI experiments can start with quite a small investment, the biggest area of investment for full production rollout is building and retaining the necessary talent. These skills include technical knowledge in specific AI technologies, data science, quality data maintenance, problem domain expertise, as well as skills to monitor, maintain and govern the environment.

Janelle Hill is a vice president and distinguished analyst at Gartner, focused on applying process thinking to optimise business outcomes. Her research helps CIOs and business transformation leaders successfully transform business operations from a people, process and technology innovation perspective.

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